awesome-llm-finetuning

This article will discuss the PEFT method in detail, exploring its benefits and how it has become an efficient way to fine-tune LLMs on downstream tasks.

RAG, Fine-tuning or Both? A Complete Framework for Choosing the Right Strategy

RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application? The definitive guide for choosing the right method for your use case

Tried and tested sample - With the availability of powerful base LLMs (e.g. LLaMA, Falcon, MPT, etc.) and instruction tuning datasets, along with the development of LoRA and QLoRA, instruction fine-tuning a base model is increasingly accessible to more people/organizations.

Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Learn when and how to use Ludwig!

Optimizing LLMs: A Step-by-Step Guide to Fine-Tuning with PEFT and QLoRA (A Practical Guide to Fine-Tuning LLM using QLora)

MLOps tools to scale your production machine learning - This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale and secure your production machine learning

Tried and tested sample - With the availability of powerful base LLMs (e.g. LLaMA, Falcon, MPT, etc.) and instruction tuning datasets, along with the development of LoRA and QLoRA, instruction fine-tuning a base model is increasingly accessible to more people/organizations.

How integrations with Deepspeed and parameter-efficient fine-tuning techniques make training LLMs with Ludwig fast and easy

An awesome & curated list of the best LLMOps tools for developers.

PEFT: Parameter-Efficient Fine-Tuning of Billion-Scale Models on Low-Resource Hardware

Low-code framework for building custom LLMs, neural networks, and other AI models

Did you know you can start trying Ludwig OpenSource on Azure How you can get started building your own LLM - How declarative ML simplifies model building and training; How to use off-the-shelf pretrained LLMs with Ludwig - the open-source declarative ML framework from Uber ; How to rapidly fine-tune an LLM on your data in less than 10 lines of code with Ludwig using parameter efficient methods, deepspeed and Ray

Ever wondered about the balance between the benefits and drawbacks of fine-tuning models? Curious about when it's the right move for customers? Look no further; we've got you covered!

This session will equip ML engineers to unlock the capabilities of LLMs like Llama-2 on for their own projects.

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Train and Deploy Amazing Models in Less Than 6 Lines of Code with Ludwig AI

Ludwig, an open source, deep learning toolbox built on top of TensorFlow that allows users to train and test machine learning models without writing code.

Reproducibility, Orchestration, Explainability.

Train with significantly fewer GPUs and avoid I/O bottlenecks. Another benefit is that we can switch between taskswhile deployed at a much lower cost by only swapping the LoRA weights as opposed to all the parameters